Converting analog signals into digital signals is typically done using discrete-time sampling, quantization and coding in an analog to digital converter. This involves measuring the amplitude of the signal at regular, discrete time intervals, often determined by the Nyquist-Shannon sampling theorem.
There are a number of disadvantages to using discrete-time digital signals. For example, in conventional discrete-time digital signal processors (DSP), the clock signal that triggers the sampling runs at a frequency that is at least twice as high as the highest frequency of interest in an input signal. This clock has to run continually at that frequency even if there is no signal or there is no high-frequency component that needs to be processed, and this can result in a significant waste of power.
In addition, conventional DSPs suffer from aliasing and quantization error. Aliasing occurs due to the lack of correspondences between the sample sequence and the continuous signal itself, resulting in distortion that is present when the signal is reconstructed from the samples. Quantization error is produced by the inaccuracies inherent in turning a continuous amplitude range of an analog input signal into discrete levels of a digitized signal, and these errors can be spread across all frequencies in a conventional DSP. Techniques such as dithering and non-uniform sampling may reduce or modify one or both of these undesired effects, but residual aliasing and/or quantization noise typically remains even after applying such techniques.